Online or onsite, instructor-led live Statistics training courses demonstrate through interactive discussion and hands-on practice how to apply Statistic principles to the solving of real-world problems.
Statistics training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Statistics training can be carried out locally on customer premises in Canada or in NobleProg corporate training centers in Canada.
NobleProg -- Your Local Training Provider
Testimonials
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course: Data Mining & Machine Learning with R
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course: Programming with Big Data in R
I really enjoyed the introduction of new packages.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course: Programming with Big Data in R
The tutor, Mr. Michael An, interacted with the audience very well, the instruction was clear. The tutor also go extent to add more information based on the requests from the students during the training.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course: Programming with Big Data in R
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course: Programming with Big Data in R
He was very informative and helpful.
Pratheep Ravy
Course: Predictive Modelling with R
I genuinely enjoyed the trainer's helping.
Urszula Kuza
Course: Tableau Advanced
I get answers on all my questions.
Natalia Gladii
Course: Data Analytics With R
The training was adaptable and personalized to our needs.
Dominique Soulie
Course: Minitab for Statistical Data Analysis
I liked the exercises as it's the only way to learn, by repetition.
David Rushe
Course: Tableau Advanced
Very tailored to needs.
Yashan Wang
Course: Data Mining with R
I genuinely enjoyed working 1:1 with Gunner.
Bryant Ives
Course: Introduction to R
I like the exercises done.
Nour Assaf
Course: Data Mining and Analysis
The hands-on exercise and the trainer capacity to explain complex topics in simple terms.
youssef chamoun
Course: Data Mining and Analysis
The information given was interesting and the best part was towards the end when we were provided with Data from Durex and worked on Data we are familiar with and perform operations to get results.
Jessica Chaar
Course: Data Mining and Analysis
I mostly liked the trainer giving real live Examples.
Simon Hahn
Course: Administrator Training for Apache Hadoop
I genuinely enjoyed the big competences of Trainer.
Grzegorz Gorski
Course: Administrator Training for Apache Hadoop
I genuinely enjoyed the many hands-on sessions.
Jacek Pieczątka
Course: Administrator Training for Apache Hadoop
I liked the new insights in deep machine learning.
Josip Arneric
Course: Neural Network in R
We gained some knowledge about NN in general, and what was the most interesting for me were the new types of NN that are popular nowadays.
Tea Poklepovic
Course: Neural Network in R
I mostly enjoyed the graphs in R :))).
Faculty of Economics and Business Zagreb
Course: Neural Network in R
I genuinely was benefit from the flexibility of the trainer.
Irina Ostapenko
Course: Statistics Level 2
The flexible and friendly style. Learning exactly what was useful and relevant for me.
Jenny Tickner
Course: Advanced R
I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.
Luke Pontin
Course: Data and Analytics - from the ground up
Learning how to use excel properly.
Torin Mitchell
Course: Data and Analytics - from the ground up
The way the trainer made complex subjects easy to understand.
Adam Drewry
Course: Data and Analytics - from the ground up
Detailed and comprehensive instruction given by experienced and clearly knowledgeable expert on the subject.
Justin Roche
Course: Data and Analytics - from the ground up
Tamil is very knowledgeable and nice person, I have learned from him a lot.
Aleksandra Szubert
Course: Data and Analytics - from the ground up
I liked the first session. Very intensive and quick.
Digital Jersey
Course: Data and Analytics - from the ground up
I mostly liked the patience of Tamil.
Laszlo Maros
Course: Data and Analytics - from the ground up
I really was benefit from the real life practical examples.
Wioleta (Vicky) Celinska-Drozd
Course: Data and Analytics - from the ground up
A lot of knowledge - theoretical and practical.
Anna Alechno
Course: Forecasting with R
I genuinely liked his knowledge and practical examples.
Irina Tulgara
Course: Forecasting with R
Overview and understanding how big the topic is.
British American Shared Services Europe BAT GBS Finance, WER/Centre/EEMEA
Course: Forecasting with R
Hands on examples were the most helpful.
Sean Kaukas
Course: Introduction to R
I enjoyed the 2nd day we did lots of examples of gauge R&R.
Vascutek Ltd
Course: Minitab for Statistical Data Analysis
I genuinely liked the exercises - use of Minicab.
Vascutek Ltd
Course: Minitab for Statistical Data Analysis
Good overview of R and good range of topics. Trainer was happy to answer all questions.
Symphony EYC
Course: R
I really enjoyed the knowledge of the trainer.
Stephanie Seiermann
Course: R
It was very informative and professionally held. Wojteks knowledge level was so advanced that he could basically answer any question and he was willing to put effort into fitting the training to my personal needs.
Sonja Steiner - BearingPoint GmbH
Course: R Programming for Data Analysis
It was very hands-on, we spent half the time actually doing things in Clouded/Hardtop, running different commands, checking the system, and so on. The extra materials (books, websites, etc. .) were really appreciated, we will have to continue to learn. The installations were quite fun, and very handy, the cluster setup from scratch was really good.
Ericsson
Course: Administrator Training for Apache Hadoop
I really liked the exercises on time series modeling.
Teleperformance
Course: Data Analytics With R
New tool which is “R” and I find it interesting to know the existence of such tool for data analysis.
Michael Lopez - Teleperformance
Course: Data Analytics With R
The tool was interesting and I see the use. I would like to learn about more about it.
Teleperformance
Course: Data Analytics With R
The training felt very personal and customised to what I wanted out of it. It was so non judgemental that I felt comfortable in asking whatever questions I needed to, and the trainer was able to competently answer all my questions. It was a very good overview of useful statistical techniques and there was a good balance between statistical theory and how to use minitab.
Cambridge Consultants
Course: Minitab for Statistical Data Analysis
Some of our clients
.png "canadian radio-television and telecommunications commission (crtc)")
Statistics Subcategories in Canada
Statistics Course Outlines in Canada
Course Name
Duration
Overview
Course Name
Duration
Overview
35 hours
Overview
This instructor-led, live training in (online or onsite) is aimed at data analysts who wish to use Stata and R to analyze big data for statistical analysis.
By the end of this training, participants will be able to:
- Create statistic models for predicting key interest variables and events.
- Generate descriptive visualizations, summary tables, frequencies, and more.
- Manage and structure large databases to preapare for data analysis.
By the end of this training, participants will be able to:
- Create statistic models for predicting key interest variables and events.
- Generate descriptive visualizations, summary tables, frequencies, and more.
- Manage and structure large databases to preapare for data analysis.
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use R to create deep learning models for finance
- Build their own deep learning stock price prediction model using R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use R to create deep learning models for finance
- Build their own deep learning stock price prediction model using R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
14 hours
Overview
Mathematica consists of 20 years of creating the most powerful specialized mathematical software engine in the world.
Its versatility makes it useful not only for doing basic academic calculations but also completing complicated calculations, like programming or numerical data presentations.
Mathematica integrates software engines doing numerical andsymbolic computation, as well as graph analysis software, programming language, document formats and the possibility of publishing your work results.
Thanks to multiplicity of its functions it’s a priceless tool for mathematicians, physicists, biologists, chemists, financial analysts, sociologists and many more professions that deal with data.
Participants will gain skills to
- perform calculations efficiently
- understanding program commands
- creating text documents
- building charts and graphs
- data presentations
Its versatility makes it useful not only for doing basic academic calculations but also completing complicated calculations, like programming or numerical data presentations.
Mathematica integrates software engines doing numerical andsymbolic computation, as well as graph analysis software, programming language, document formats and the possibility of publishing your work results.
Thanks to multiplicity of its functions it’s a priceless tool for mathematicians, physicists, biologists, chemists, financial analysts, sociologists and many more professions that deal with data.
Participants will gain skills to
- perform calculations efficiently
- understanding program commands
- creating text documents
- building charts and graphs
- data presentations
42 hours
Overview
Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions:
What has happened?
- processing and analyzing data
- producing informative data visualizations
What will happen?
- forecasting future performance
- evaluating forecasts
What should happen?
- turning data into evidence-based business decisions
- optimizing processes
The course itself can be delivered either as a 6 day classroom course or [remotely](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
What has happened?
- processing and analyzing data
- producing informative data visualizations
What will happen?
- forecasting future performance
- evaluating forecasts
What should happen?
- turning data into evidence-based business decisions
- optimizing processes
The course itself can be delivered either as a 6 day classroom course or [remotely](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
14 hours
Overview
Scilab is a well-developed, free, and open-source high-level language for scientific data manipulation. Used for statistics, graphics and animation, simulation, signal processing, physics, optimization, and more, its central data structure is the matrix, simplifying many types of problems compared to alternatives such as FORTRAN and C derivatives. It is compatible with languages such as C, Java, and Python, making it suitable as for use as a supplement to existing systems.
In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing.
By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge.
Audience
- Data scientists and engineers, especially with interest in image processing and facial recognition
Format of the course
- Part lecture, part discussion, exercises and intensive hands-on practice, with a final project
In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing.
By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge.
Audience
- Data scientists and engineers, especially with interest in image processing and facial recognition
Format of the course
- Part lecture, part discussion, exercises and intensive hands-on practice, with a final project
21 hours
Overview
In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.
By the end of this training, participants will be able to:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
By the end of this training, participants will be able to:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
7 hours
Overview
The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.
In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.
By the end of this training, participants will be able to:
- Perform data analysis and create appealing visualizations
- Draw useful conclusions from various datasets of sample data
- Filter, sort and summarize data to answer exploratory questions
- Turn processed data into informative line plots, bar plots, histograms
- Import and filter data from diverse data sources, including Excel, CSV, and SPSS files
Audience
- Beginners to the R language
- Beginners to data analysis and data visualization
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.
By the end of this training, participants will be able to:
- Perform data analysis and create appealing visualizations
- Draw useful conclusions from various datasets of sample data
- Filter, sort and summarize data to answer exploratory questions
- Turn processed data into informative line plots, bar plots, histograms
- Import and filter data from diverse data sources, including Excel, CSV, and SPSS files
Audience
- Beginners to the R language
- Beginners to data analysis and data visualization
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.
Audience
- Developers
- Data scientists
- Banking professionals with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.
Audience
- Developers
- Data scientists
- Banking professionals with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
- Understand the fundamentals of the R programming language
- Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate deploy and optimize an R application
Audience
- Developers
- Analysts
- Quants
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
- Understand the fundamentals of the R programming language
- Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate deploy and optimize an R application
Audience
- Developers
- Analysts
- Quants
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
21 hours
Overview
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn the fundamentals of R programming as they walk through coding in R using financial examples.
By the end of this training, participants will be able to:
- Understand the basics of R programming
- Use R to manipulate their data to perform basic financial operations
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of R programming as they walk through coding in R using financial examples.
By the end of this training, participants will be able to:
- Understand the basics of R programming
- Use R to manipulate their data to perform basic financial operations
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn the basics of financial trading as they step through building and implementing basic trading strategies and actions in R using quantstrat.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in trading
- Create and implement their first trading strategy using R
- Analyze the performance of their strategy using R
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the basics of financial trading as they step through building and implementing basic trading strategies and actions in R using quantstrat.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in trading
- Create and implement their first trading strategy using R
- Analyze the performance of their strategy using R
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn advanced programming concepts in R as they walk through coding in R using financial examples.
By the end of this training, participants will be able to:
- Implement advanced R programming techniques
- Use R to manipulate their data to perform more advanced financial operations
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn advanced programming concepts in R as they walk through coding in R using financial examples.
By the end of this training, participants will be able to:
- Implement advanced R programming techniques
- Use R to manipulate their data to perform more advanced financial operations
Audience
- Programmers
- Finance professionals
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use R to create deep learning models for banking
- Build their own deep learning credit risk model using R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use R to create deep learning models for banking
- Build their own deep learning credit risk model using R
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions
7 hours
Overview
Shiny is an open source R package that provides a web framework for building interactive web applications using R.
In this instructor-led, live training, participants will learn how to combine data science and web development using Shiny, R, and HTML.
By the end of this training, participants will be able to:
- Build interactive web applications with R using Shiny
Audience
- Data scientists
- Web developers
- Statisticians
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to combine data science and web development using Shiny, R, and HTML.
By the end of this training, participants will be able to:
- Build interactive web applications with R using Shiny
Audience
- Data scientists
- Web developers
- Statisticians
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
Organization – Lab work and practical examples on real data
7 hours
Overview
The objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data.
14 hours
Overview
Tableau helps people see and understand data.
14 hours
Overview
This instructor-led, live training (online or onsite) is aimed at HR professionals who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
14 hours
Overview
This instructor-led, live training (online or onsite) is aimed at HR professionals and recruitment specialists who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
14 hours
Overview
This instructor-led, live training in Canada (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.
By the end of this training, participants will be able to:
- Employ algorithms to buy and sell securities at specialized increments rapidly.
- Reduce costs associated with trade using algorithmic trading.
- Automatically monitor stock prices and place trades.
By the end of this training, participants will be able to:
- Employ algorithms to buy and sell securities at specialized increments rapidly.
- Reduce costs associated with trade using algorithmic trading.
- Automatically monitor stock prices and place trades.
21 hours
Overview
This instructor-led, live training in Canada (online or onsite) is aimed at data analysts who wish to program in R for Excel.
By the end of this training, participants will be able to:
- Toggle and move data between Excel and R.
- Use R Tidyverse and R features for data analytic solutions in Excel.
- Extend their data analytical skills by learning R.
By the end of this training, participants will be able to:
- Toggle and move data between Excel and R.
- Use R Tidyverse and R features for data analytic solutions in Excel.
- Extend their data analytical skills by learning R.
14 hours
Overview
This instructor-led, live training in Canada (online or onsite) is aimed at data scientists who wish to program in R for Tableau.
By the end of this training, participants will be able to:
- Implement Tableau analytics with R.
- Return values to Tableau with learning algorithms in R.
- Structure and visualize R functions in Tableau.
- Make data driven decisions for business operations.
By the end of this training, participants will be able to:
- Implement Tableau analytics with R.
- Return values to Tableau with learning algorithms in R.
- Structure and visualize R functions in Tableau.
- Make data driven decisions for business operations.
14 hours
Overview
This instructor-led, live training in Canada (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME.
By the end of this training, participants will be able to:
- Plan, build, and deploy machine learning models in KNIME.
- Make data driven decisions for operations.
- Implement end to end data science projects.
By the end of this training, participants will be able to:
- Plan, build, and deploy machine learning models in KNIME.
- Make data driven decisions for operations.
- Implement end to end data science projects.
14 hours
Overview
This instructor-led, live training in Canada (online or onsite) is aimed at data analysts who wish to program with R in SAS for cluster analysis.
By the end of this training, participants will be able to:
- Use cluster analysis for data mining
- Master R syntax for clustering solutions.
- Implement hierarchical and non-hierarchical clustering.
- Make data-driven decisions to help to improve business operations.
By the end of this training, participants will be able to:
- Use cluster analysis for data mining
- Master R syntax for clustering solutions.
- Implement hierarchical and non-hierarchical clustering.
- Make data-driven decisions to help to improve business operations.
14 hours
Overview
This instructor-led, live training in Canada (online or onsite) is aimed at data scientists and data analysts who wish to program in R and Python for outlier detection.
By the end of this training, participants will be able to:
- Identify whether data is an anomaly or is an expected value.
- Implement algorithms for anomaly detection.
- Use various techniques and methods to detect anomalies.
By the end of this training, participants will be able to:
- Identify whether data is an anomaly or is an expected value.
- Implement algorithms for anomaly detection.
- Use various techniques and methods to detect anomalies.
14 hours
Overview
This instructor-led, live training in Canada (online or onsite) is aimed at data analysts who wish to apply Minitab statistical analysis to Six Sigma projects.
By the end of this training, participants will be able to:
- Use Minitab for performing advanced statistical analysis.
- Apply Six Sigma methodology to specific projects.
- Gain knowledge on Six Sigma projects across industries.
By the end of this training, participants will be able to:
- Use Minitab for performing advanced statistical analysis.
- Apply Six Sigma methodology to specific projects.
- Gain knowledge on Six Sigma projects across industries.
Upcoming Statistics Courses in Canada
2021-04-27 09:30:00
7 hours
2021-05-10 09:30:00
14 hours
2021-05-31 09:30:00
21 hours
2021-06-08 09:30:00
14 hours
2021-06-16 09:30:00
14 hours
Other regions in Canada
Other countries
Consulting in Canada
Online Statistics courses, Weekend Statistics courses, Evening Statistics training, Statistics boot camp, Statistics instructor-led, Weekend Statistics training, Evening Statistics courses, Statistics coaching, Statistics instructor, Statistics trainer, Statistics training courses, Statistics classes, Statistics on-site, Statistics private courses, Statistics one on one training